Automatic feature extraction from airborne lidar measurements to identify cross-shore morphologies indicative of beach erosion

Airborne lidar data were acquired along St. Augustine Beach, Florida six times between August 2003 and June 2006. To identify sub-aerial morphologies indicative to beach erosion, the data sets were mined extensively by extracting several morphological features using cross-shore profile sampling. For each profile, the features were grouped into erosion or accretion classes and their class-conditional probability density functions (PDFs) estimated via Parzen windowing. PDF separability was ranked using symmetric and normalized measures of relative entropy (i.e. divergence). Results were compared to a simple median metric. The more interclass separation provided by a feature, the greater its potential as an indicator for erosion or accretion. Over short time periods (>1 month), beach slope and beach width ranked highest by providing the most separation and therefore high potential as indicators for erosion. Over longer time periods (>1 year), deviation-from-trend, which is the shoreline\´s deviation from the natural strike of the beach, ranked highest. This is significant in that the pier region\´s deviation from the natural trend is believed by coastal researchers to be a strong contributing factor to it being an erosion "hot spot". The method we have developed provides a systematic framework to mine high-resolution airborne lidar data over beaches, detect erosion-prone areas, and numerically rank a feature\´s potential as an indicator for erosion.